Data Scenario and Model Hypothesis

Standard fit report for fits of SISCA to WCVI_Herring data.

Data Scenario: WCVI_agg

Model Hypothesis: baseAggModel

Species:

Stocks:

Final phase convergence diagnostics

Max Gradient: 0.0212524

Objective Function value: 4.5404989^{4}

Time to fit model: 1

PD Hessian: FALSE

No. of Non-finite SEs: 0

Model fits

At-a-glance

Time series of spawning biomass with scaled spawn indices (top),
recruitments (second row), natural mortality (third row), and harvest rates (bottom row) for 
substocks of WCVI_Herring. Stocks are, from left to right,WCVI.

Figure 1: Time series of spawning biomass with scaled spawn indices (top), recruitments (second row), natural mortality (third row), and harvest rates (bottom row) for substocks of WCVI_Herring. Stocks are, from left to right,WCVI.

Fits to data

Model fits to spawn indices.

Figure 2: Model fits to spawn indices.

Probability of detection of a spawning event,
with respect to spawning stock biomass. Lines show the modeled probability
and the points indicate whether a spawn was detected, with 0 
for no spawn detected, and 1 for a positive index.

Figure 3: Probability of detection of a spawning event, with respect to spawning stock biomass. Lines show the modeled probability and the points indicate whether a spawn was detected, with 0 for no spawn detected, and 1 for a positive index.

Average model fits to age data. Stocks are left to right, 
and gears are top to bottom.

Figure 4: Average model fits to age data. Stocks are left to right, and gears are top to bottom.

Model fits to age data, averaged over stock and time. Gears are top to bottom.

Figure 5: Model fits to age data, averaged over stock and time. Gears are top to bottom.

Table 1: Estimated standard deviations for observational data. The first three columns show age data sampling error standard deviations from the logistic-normal compositional likelihood function, and the last column shows spawn survey index standard deviations on the log scale.
\(\tau^{age}_{Red}\) \(\tau^{age}_{SR}\) \(\tau^{age}_{Gn}\) \(\tau^{surv}_{Su}\) \(\tau^{surv}_{D}\)
WCVI 0.754 0.485 0.669 0.5 0.5

Recruitment

Age-1 recruitments for all stocks. Equilibrium unfished recruitment $R_0$ is 
indicated by the horizontal dashed line. Second row shows recruitment residuals on the log scale, 
with the average of estimated residuals shown by the horizontal red dashed line.

Figure 6: Age-1 recruitments for all stocks. Equilibrium unfished recruitment \(R_0\) is indicated by the horizontal dashed line. Second row shows recruitment residuals on the log scale, with the average of estimated residuals shown by the horizontal red dashed line.

Stock-recruit curves (solid lines) and modeled recruitments (coloured points)

Figure 7: Stock-recruit curves (solid lines) and modeled recruitments (coloured points)

Natural Mortality

Mt

Figure 8: Mt

densityDependent M

Figure 9: densityDependent M

Total natural mortality at age

Figure 10: Total natural mortality at age

Total mortality at age

Figure 11: Total mortality at age

Selectivity and Catch

Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet, and the dead ponded fish shown with a red border.

Figure 12: Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet, and the dead ponded fish shown with a red border.

Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet, and the dead ponded fish shown with a red border.

Figure 13: Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet, and the dead ponded fish shown with a red border.

Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet, and the dead ponded fish shown with a red border.

Figure 14: Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet, and the dead ponded fish shown with a red border.

Selectivity-at-age for each fleet (rows). Aggregate stock average selectivity curves are shown as thick grey lines, while sub-stock specific estimates are shown as dashed thin coloured lines.

Figure 15: Selectivity-at-age for each fleet (rows). Aggregate stock average selectivity curves are shown as thick grey lines, while sub-stock specific estimates are shown as dashed thin coloured lines.

Reference Points

Yield Curves

Equilibrium yield curves as a function of fishing mortality rates, assuming all fishing mortality comes from the gillnet fleet.

Figure 16: Equilibrium yield curves as a function of fishing mortality rates, assuming all fishing mortality comes from the gillnet fleet.

Stock specific fits

WCVI

Age composition fits

Model fits to yearly  WCVI  stock age compositions for the  reduction  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 17: Model fits to yearly WCVI stock age compositions for the reduction fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Model fits to yearly  WCVI  stock age compositions for the  seineRoe  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 18: Model fits to yearly WCVI stock age compositions for the seineRoe fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Model fits to yearly  WCVI  stock age compositions for the  gillnet  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 19: Model fits to yearly WCVI stock age compositions for the gillnet fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Age composition residuals for the  WCVI sub-stock. Positive residuals are black  black, while negative residuals are red.

Figure 20: Age composition residuals for the WCVI sub-stock. Positive residuals are black black, while negative residuals are red.

Age composition post tail compression

Model fits to tail compressed yearly  WCVI  stock age compositions for the  reduction  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 21: Model fits to tail compressed yearly WCVI stock age compositions for the reduction fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Model fits to tail compressed yearly  WCVI  stock age compositions for the  seineRoe  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 22: Model fits to tail compressed yearly WCVI stock age compositions for the seineRoe fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Model fits to tail compressed yearly  WCVI  stock age compositions for the  gillnet  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 23: Model fits to tail compressed yearly WCVI stock age compositions for the gillnet fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Optimisation performance

Objective function components

Table 2: Objective function components for data observations.
objFun obsSurface obsDive ageRed ageSR ageGill
Total 45404.99 0 0 27.69 4.46 -14.8
WCVI 45404.99 0 0 27.69 4.46 -14.8
Table 3: Objective function components for standard (single level) and hyper-priors.
V1
objFun 45404.990000
recDevs 67.330000
initDevs 10.590000
h 17.430000
M -2.080000
tvMdev 0.000000
IGtau_surf 2.060000
IGtau_dive 3.170000
tvSelAlpha 0.000000
tvSelBeta 0.000000
selAlphaRed -1.380000
selAlphaSR -1.380000
selAlphaGn -1.380000
selBetaRed -1.380000
selBetaSR -1.380000
selBetaGn -1.380000
lnB0 4.628946
lnRinit 6.476646
psiSOK -6.750000
Table 4: Objective function components for hierarchical (mult-level) priors.
V1
objFun 45404.99
MDev 0.00
hDev 0.92
selAlphaDevR 0.00
selAlphaDevSR 0.00
selAlphaDevGn 0.00
selBetaDevR 0.00
selBetaDevSR 0.00
selBetaDevGn 0.00

Phase fit table

Table 5: Optimisation performance of SISCA for each phase.
phase objFun maxGrad nPar convCode convMsg time pdHess
1 46002.65 0.0955823 4 0 relative convergence (4) 0.0958667 FALSE
2 45993.06 0.0128742 14 0 relative convergence (4) 0.0764167 FALSE
3 45993.06 0.0054366 14 0 relative convergence (4) 0.0666000 FALSE
4 45965.21 0.0170467 17 0 relative convergence (4) 0.0789833 FALSE
5 45642.21 0.0201821 86 0 relative convergence (4) 0.0958500 FALSE
6 45467.25 0.0290619 151 0 relative convergence (4) 0.1857333 FALSE
7 45467.25 0.0112788 151 0 relative convergence (4) 0.0808833 FALSE
8 45405.14 0.1349876 152 0 relative convergence (4) 0.1660500 FALSE
9 45404.99 0.0212524 153 0 relative convergence (4) 0.1504167 FALSE
RE NA NA NA NA NA NA NA

Leading Parameter SDReport

Table 6: SD report showing leading parameter estimates, standard errors, gradient components, and coefficients of variation. Gradients with a magnitude above 1e-3 are shown in bold red, while the coefficients of variation (cv) are
coloured so that smaller values are lighter in colour, and larger values are darker, with cvs above .5 in bold, and cvs above 3 in red.
est se gr cv
lnB0_p 4.6289 0.1737 0.00064 0.0375
lnRinit_p 6.4766 0.1638 0.0031 0.0253
lnRbar_p 7.2315 0.0981 -0.035 0.0136
logit_ySteepness 0.6001 0.3780 0.0012 0.6298
lnM -0.6933 0.0323 0.075 0.0466
fDevs_ap 0.8212 0.2628 -2e-05 0.3201
fDevs_ap.1 0.6901 0.2802 0.00061 0.4061
fDevs_ap.2 0.3655 0.3956 0.00073 1.0824
fDevs_ap.3 0.8556 0.4144 0.00098 0.4843
fDevs_ap.4 -0.6305 0.6955 0.00017 1.1032
fDevs_ap.5 -0.4557 0.7465 0.00012 1.638
fDevs_ap.6 -0.3092 0.8366 7.4e-05 2.7061
fDevs_ap.7 -0.1999 0.8878 5.4e-05 4.4409
fDevs_ap.8 -0.1210 0.9360 2.2e-05 7.7357
fDevs_ap.9 -0.1561 0.9219 1.7e-05 5.905
recDevs_pt 0.0609 0.0827 -0.0016 1.3582
recDevs_pt.1 0.0572 0.0922 -0.00012 1.6112
recDevs_pt.2 0.0458 0.0955 -0.0016 2.0877
recDevs_pt.3 0.1076 0.0997 -0.005 0.9258
recDevs_pt.4 0.2873 0.0862 -0.0039 0.2999
recDevs_pt.5 0.3288 0.0793 -0.0075 0.2412
recDevs_pt.6 0.1742 0.1063 -0.0025 0.6101
recDevs_pt.7 0.0673 0.1077 -0.00044 1.5992
recDevs_pt.8 0.1482 0.1049 -0.00088 0.7076
recDevs_pt.9 0.0385 0.1200 0.0011 3.1142
recDevs_pt.10 0.0719 0.1139 0.0012 1.5831
recDevs_pt.11 -0.0990 0.1194 0.0013 1.2058
recDevs_pt.12 -0.1602 0.1089 0.0022 0.68
recDevs_pt.13 -0.0310 0.0955 0.004 3.0761
recDevs_pt.14 -0.2190 0.1276 9.4e-06 0.5824
recDevs_pt.15 -0.1928 0.1282 0.00024 0.6652
recDevs_pt.16 -0.0134 0.1060 0.00098 7.9283
recDevs_pt.17 0.2353 0.0955 0.0027 0.4059
recDevs_pt.18 0.2410 0.0882 0.0031 0.3662
recDevs_pt.19 0.2224 0.0846 -0.00077 0.3803
recDevs_pt.20 0.3176 0.0837 -0.0023 0.2636
recDevs_pt.21 0.4797 0.0831 -0.0049 0.1732
recDevs_pt.22 0.2160 0.0888 -0.0035 0.4113
recDevs_pt.23 0.0969 0.0918 -0.0038 0.9478
recDevs_pt.24 0.3902 0.0907 0.0042 0.2325
recDevs_pt.25 -0.0300 0.0980 0.00046 3.2686
recDevs_pt.26 0.3669 0.0952 0.0025 0.2595
recDevs_pt.27 0.2070 0.0977 -0.0078 0.4718
recDevs_pt.28 0.1020 0.1009 -0.0016 0.9891
recDevs_pt.29 -0.0360 0.0988 0.0036 2.7444
recDevs_pt.30 0.0909 0.0976 0.0023 1.0737
recDevs_pt.31 0.3155 0.0959 0.0027 0.3039
recDevs_pt.32 0.3276 0.0920 0.0028 0.2808
recDevs_pt.33 -0.0369 0.0851 -0.0013 2.3083
recDevs_pt.34 0.4896 0.0859 0.0037 0.1754
recDevs_pt.35 -0.1229 0.0891 -0.0075 0.7249
recDevs_pt.36 -0.0180 0.0845 -0.0016 4.7038
recDevs_pt.37 -0.0828 0.0862 -0.00076 1.0416
recDevs_pt.38 0.1730 0.0809 0.00023 0.4679
recDevs_pt.39 0.0720 0.0850 -0.0072 1.1805
recDevs_pt.40 -0.0204 0.0854 -0.0041 4.1946
recDevs_pt.41 -0.2570 0.0862 0.00074 0.3354
recDevs_pt.42 -0.1398 0.0843 -0.0085 0.603
recDevs_pt.43 0.2872 0.0794 -0.013 0.2764
recDevs_pt.44 -0.0437 0.0848 -0.01 1.9412
recDevs_pt.45 -0.2874 0.0909 -0.0066 0.3164
recDevs_pt.46 -0.1330 0.0879 -0.0021 0.6608
recDevs_pt.47 -0.0849 0.0855 -0.0013 1.0073
recDevs_pt.48 0.0404 0.0783 -0.004 1.9375
recDevs_pt.49 0.0835 0.0766 -0.00017 0.9171
recDevs_pt.50 -0.0337 0.0889 -0.005 2.6366
recDevs_pt.51 -0.1558 0.0904 -0.001 0.5805
recDevs_pt.52 -0.2887 0.0994 -0.00092 0.3442
recDevs_pt.53 -0.2284 0.0875 -0.0036 0.3832
recDevs_pt.54 -0.2845 0.0900 -0.003 0.3163
recDevs_pt.55 -0.1711 0.0767 0.0041 0.4484
recDevs_pt.56 -0.1778 0.0824 -0.00075 0.4635
recDevs_pt.57 -0.0125 0.0693 -0.0035 5.5236
recDevs_pt.58 -0.1347 0.0841 -0.00024 0.6245
recDevs_pt.59 -0.1562 0.0825 -0.00064 0.5281
recDevs_pt.60 -0.0043 0.0730 -0.00069 17.1329
recDevs_pt.61 0.0528 0.0691 -0.0019 1.3094
recDevs_pt.62 0.0182 0.0736 0.0025 4.0419
recDevs_pt.63 -0.1644 0.0932 0.0014 0.567
recDevs_pt.64 -0.0931 0.0865 0.0019 0.9292
omegaM_pt 0.0169 0.8480 0.0071 50.3158
omegaM_pt.1 0.0480 0.8848 0.0072 18.4353
omegaM_pt.2 0.0655 0.9031 0.0072 13.7822
omegaM_pt.3 0.0098 0.9031 0.0071 91.7251
omegaM_pt.4 -0.0137 0.9081 0.0068 66.2605
omegaM_pt.5 0.0439 0.9148 0.0066 20.8415
omegaM_pt.6 0.1330 0.9318 0.0063 7.0061
omegaM_pt.7 0.2417 0.9243 0.006 3.8245
omegaM_pt.8 0.4292 0.9008 0.0057 2.099
omegaM_pt.9 0.4717 0.9000 0.0057 1.9081
omegaM_pt.10 0.4061 0.8991 0.0057 2.2142
omegaM_pt.11 0.4288 0.8671 0.0057 2.0224
omegaM_pt.12 0.3615 0.8293 0.0059 2.2942
omegaM_pt.13 0.2493 0.8333 0.0061 3.3427
omegaM_pt.14 0.0552 0.9094 0.0064 16.473
omegaM_pt.15 -0.1361 0.8875 0.0066 6.52
omegaM_pt.16 -0.2713 0.8377 0.0065 3.0875
omegaM_pt.17 -0.4137 0.8920 0.0064 2.1561
omegaM_pt.18 -0.5054 0.9198 0.0064 1.8198
omegaM_pt.19 -0.5333 0.9325 0.0065 1.7485
omegaM_pt.20 -0.5567 0.9354 0.0067 1.6802
omegaM_pt.21 -0.5105 0.9299 0.0068 1.8216
omegaM_pt.22 -0.2829 0.9212 0.0069 3.2567
omegaM_pt.23 0.0831 0.9158 0.0068 11.0253
omegaM_pt.24 0.4098 0.9145 0.0067 2.2317
omegaM_pt.25 0.6511 0.9175 0.0065 1.4092
omegaM_pt.26 0.6719 0.9201 0.0064 1.3694
omegaM_pt.27 0.6924 0.9188 0.0065 1.327
omegaM_pt.28 0.6800 0.9248 0.0063 1.36
omegaM_pt.29 0.5284 0.9347 0.0062 1.7692
omegaM_pt.30 0.2550 0.9356 0.0061 3.6691
omegaM_pt.31 -0.0622 0.9316 0.0061 14.9751
omegaM_pt.32 -0.3183 0.9283 0.0063 2.9167
omegaM_pt.33 -0.4786 0.9298 0.0066 1.9429
omegaM_pt.34 -0.6408 0.9294 0.0067 1.4504
omegaM_pt.35 -0.4821 0.9251 0.0069 1.9189
omegaM_pt.36 -0.3030 0.9214 0.0069 3.0407
omegaM_pt.37 -0.1468 0.9219 0.0069 6.281
omegaM_pt.38 0.0009 0.9211 0.0068 1035.5102
omegaM_pt.39 0.1283 0.9231 0.0067 7.1959
omegaM_pt.40 0.1584 0.9262 0.0066 5.8472
omegaM_pt.41 0.1458 0.9308 0.0063 6.3856
omegaM_pt.42 0.0927 0.9303 0.006 10.036
omegaM_pt.43 0.0483 0.9281 0.0056 19.2034
omegaM_pt.44 0.1313 0.9282 0.005 7.0672
omegaM_pt.45 0.2386 0.9256 0.0042 3.8789
omegaM_pt.46 0.2834 0.9208 0.0033 3.2496
omegaM_pt.47 0.2309 0.9241 0.0026 4.0023
omegaM_pt.48 -0.0007 0.9337 0.002 1359.7599
omegaM_pt.49 -0.2199 0.9403 0.0015 4.2766
omegaM_pt.50 -0.2656 0.9425 0.0012 3.5492
omegaM_pt.51 -0.2240 0.9421 0.00094 4.206
omegaM_pt.52 -0.2627 0.9447 7e-04 3.5957
omegaM_pt.53 -0.4825 0.9482 5e-04 1.9653
omegaM_pt.54 -0.7410 0.9504 0.00029 1.2826
omegaM_pt.55 -0.9050 0.9515 6e-05 1.0514
omegaM_pt.56 -0.8465 0.9508 4.1e-06 1.1233
omegaM_pt.57 -0.6492 0.9517 -1.5e-05 1.466
omegaM_pt.58 -0.4425 0.9541 -4.8e-05 2.156
omegaM_pt.59 -0.3202 0.9562 -9.5e-05 2.9865
omegaM_pt.60 -0.1885 0.9587 -0.00015 5.0862
omegaM_pt.61 -0.1018 0.9610 -0.00019 9.4448
omegaM_pt.62 -0.0165 0.9636 -2e-04 58.3323
omegaM_pt.63 0.0512 0.9676 -0.00018 18.8953
omegaM_pt.64 0.1117 0.9714 -2e-04 8.6967
omegaM_pt.65 0.1796 0.9752 -0.00022 5.4282
omegaM_pt.66 0.1378 0.9927 -0.00016 7.203
omegaM_pt.67 0.0578 1.0009 -7.5e-05 17.3215
lnqComb_pg -0.9712 0.1112 -0.00063 0.1145
lnqComb_pg.1 -0.0036 0.0100 -0.002 2.7783
logitphi1_g 0.4076 0.3937 0.00016 0.9659
logitphi1_g.1 0.2732 0.2106 -0.00035 0.771
logitphi1_g.2 1.7081 0.3864 -0.0076 0.2262

MCMC posteriors

MCMC performance

## Not Yet Implemented

Other

Compositional Likelihood Correlation Matrices

Estimated correlation matrices for age composition residuals in the  reduction  fleet. The circles above the visualise the numbers below the diagonal.

Figure 24: Estimated correlation matrices for age composition residuals in the reduction fleet. The circles above the visualise the numbers below the diagonal.

Estimated correlation matrices for age composition residuals in the  seineRoe  fleet. The circles above the visualise the numbers below the diagonal.

Figure 25: Estimated correlation matrices for age composition residuals in the seineRoe fleet. The circles above the visualise the numbers below the diagonal.

Estimated correlation matrices for age composition residuals in the  gillnet  fleet. The circles above the visualise the numbers below the diagonal.

Figure 26: Estimated correlation matrices for age composition residuals in the gillnet fleet. The circles above the visualise the numbers below the diagonal.

Comparisons with ISCAM

Plots of average age composition fits at the major stock level. Left is SISCA, right is ISCAM.

Figure 27: Plots of average age composition fits at the major stock level. Left is SISCA, right is ISCAM.

Comparison of spawning stock biomass and age-2 recruitment at the major stock level between ISCAM and SISCA.

Figure 28: Comparison of spawning stock biomass and age-2 recruitment at the major stock level between ISCAM and SISCA.